1. Field of the Disclosed Embodiments
This disclosure relates to systems and methods for providing precise location sensing for wireless devices in an indoor environment using channel fading fingerprinting.
2. Related Art
In recent years, there has been an unprecedented increase in the numbers and types of wireless devices accessing all manner of wireless networks. These wireless devices are being loaded with increasingly capable libraries of applications that provide the wireless devices with the capacity to undertake all manner of tasks that far exceed simple communication between wireless devices via, for example, a wireless network to which the wireless devices have gained access.
Many of the applications that are being developed and implemented in wireless devices, and the wireless networks to which they are connected, benefit from an ability to identify a specific geographic location for a particular wireless device. These so called “location-based services” are provided in the wireless device based on the ability of the wireless device to identify its own geographic location.
Conventional position locating of a wireless device is often accomplished with reference to the Global Positioning Satellite (GPS) System. Most common wireless devices include a GPS receiver for precisely this purpose. A position of the wireless device that is determined by referencing a GPS signal is generally accurate to within about 50 feet (15 m). This accuracy may be enough for certain location based services in open areas. In order to take advantage of more advanced location-based services, particularly in indoor environments, more precise position locating accuracies for wireless devices are desired.
The requirement for more precise position accuracies increases with regard to indoor environment. When a wireless device is moved indoors, a first problem with which a user of the wireless device is confronted in attempting to obtain any positional reference is that the GPS, as the only globally available locating system, typically fails to work in the indoor environment. This is largely due to the comparatively weak signal strength of the satellite signals in the GPS system, or the indoor multi-path environment severely degrading GPS performance. Even if GPS satellite signals were available, however, a generic 50 foot precision to locating the wireless device with an available GPS signal would simply not be nearly accurate enough in an indoor environment.
There has previously been a body of research concerned with precise indoor position location undertaken for purposes other than locating wireless devices. Among the early indoor location sensing techniques that were attempted were techniques that relied on deploying specialized communications and sensing infrastructures within the particular indoor environment. Examples of the earliest attempts at indoor location sensing techniques in general included use of infrared beacons and/or ultrasound devices for providing accurate indoor localization. A significant drawback to these early solutions was in their specificity. Often, practical deployment of these types of indoor localization systems in a particular indoor environment was hindered by the significant costs in time and resources associated with the initial deployment and with the requirements for ongoing maintenance of such systems.
With the proliferation of wireless networks, such as, for example, Wi-Fi networks, indoor localization techniques for providing locations for wireless devices in the indoor environment have been separately studied. These techniques often make reference to Wi-Fi signal strengths, often referred to as Received Signal Strength Indication or RSSI, that are sensed by the wireless (Wi-Fi) receiver in the wireless devices. These techniques are often based on signal propagation models. Examples of these common techniques are Place Lab® and SkyHook®, which use Media Access Control addresses for nearby wireless access points to determine a position of a mobile device. These techniques, however, fail to meet modern accuracy requirements due to potentially large variations in signal strengths in indoor environments. These techniques are advertised to have location errors of approximately 20 m. Other techniques using Wi-Fi may may require extensive pre-use effort, for example, in building detailed maps or propagation models for multiple wireless access points based on surveys of a particular indoor environment. The basic approach in these techniques is based on a requirement to populate a database with signal strength fingerprints for each location in the indoor environment from multiple wireless access points. In fact, the greater the number of wireless access points, the better the accuracy of these signal strength mapping techniques. These signal strength fingerprints include, for example, a vector of received signal strength indication measurements from various available wireless access points for each location in the indoor environment. A particular wireless device is then localized by matching the observed signal strength readings against the signal strength fingerprint database. As indicated above, these schemes require considerable manual efforts to perform the detailed measurements across the indoor space and to maintain the signal map over time as they require access for the particular wireless device to multiple wireless access points. More sophisticated signal fingerprinting schemes have been attempted. One such scheme is described in a paper by Chintalapudi et al. entitled “Indoor Localization Without the Pain,” (In Proc. of ACM Mobicom, 2010). The Chintalapudi technique also uses sets of signal strengths as the location signature. Even the more advanced signal strength based system discussed in the Chintalapudi paper, however, can only provide location accuracies in a range of several meters, making them still not accurate enough for certain more advanced mobile applications. Estimating more precise indoor location remains an unresolved problem.